Fast Generation of High Fidelity RGB-D Images by Deep-Learning with Adaptive Convolution

12 February 2020

Papers citing "Fast Generation of High Fidelity RGB-D Images by Deep-Learning with Adaptive Convolution"

7 / 7 papers shown

Title
Depth-aware CNN for RGB-D Segmentation Weiyue Wang Ulrich Neumann SSeg 119 256 0 19 Mar 2018
Residual Dense Network for Image Super-Resolution Yulun Zhang Yapeng Tian Yu Kong Bineng Zhong Y. Fu SupR 103 3,299 0 24 Feb 2018
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network C. Ledig Lucas Theis Ferenc Huszár Jose Caballero Andrew Cunningham ... Andrew P. Aitken Alykhan Tejani J. Totz Zehan Wang Wenzhe Shi GAN 215 10,646 0 15 Sep 2016
Perceptual Losses for Real-Time Style Transfer and Super-Resolution Justin Johnson Alexandre Alahi Li Fei-Fei SupR 155 10,202 0 27 Mar 2016
Accurate Image Super-Resolution Using Very Deep Convolutional Networks Jiwon Kim Jung Kwon Lee Kyoung Mu Lee SupR 83 6,164 0 14 Nov 2015
Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields Fayao Liu Chunhua Shen Guosheng Lin Ian Reid MDE 77 1,196 0 26 Feb 2015
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture David Eigen Rob Fergus VLM MDE 117 2,674 0 18 Nov 2014